Modified dendrite morphological neural network applied to 3D object recognition

Humberto Sossa; Elizabeth Guevara

doi:10.1007/978-3-642-38989-4_32

Modified dendrite morphological neural network applied to 3D object recognition

Humberto Sossa, Elizabeth Guevara

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

Abstract

In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.

Original language	English
Title of host publication	Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings
Pages	314-324
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-642-38989-4_32
State	Published - 2013
Event	5th Mexican Conference on Pattern Recognition, MCPR 2013 - Queretaro, Mexico Duration: 26 Jun 2013 → 29 Jun 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7914 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th Mexican Conference on Pattern Recognition, MCPR 2013
Country/Territory	Mexico
City	Queretaro
Period	26/06/13 → 29/06/13

Keywords

3D object recognition
Dendrite morphological neural network
classification
efficient training

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10.1007/978-3-642-38989-4_32

Cite this

Sossa, H., & Guevara, E. (2013). Modified dendrite morphological neural network applied to 3D object recognition. In Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings (pp. 314-324). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7914 LNCS). https://doi.org/10.1007/978-3-642-38989-4_32

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title = "Modified dendrite morphological neural network applied to 3D object recognition",

abstract = "In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.",

keywords = "3D object recognition, Dendrite morphological neural network, classification, efficient training",

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Sossa, H & Guevara, E 2013, Modified dendrite morphological neural network applied to 3D object recognition. in Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7914 LNCS, pp. 314-324, 5th Mexican Conference on Pattern Recognition, MCPR 2013, Queretaro, Mexico, 26/06/13. https://doi.org/10.1007/978-3-642-38989-4_32

Modified dendrite morphological neural network applied to 3D object recognition. / Sossa, Humberto; Guevara, Elizabeth.
Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings. 2013. p. 314-324 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7914 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Sossa, Humberto

AU - Guevara, Elizabeth

PY - 2013

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N2 - In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.

AB - In this paper a modified dendrite morphological neural network (DMNN) is applied for recognition and classification of 3D objects. For feature extraction, the first two Hu's moment invariants are calculated based on 2D binary images, as well as the mean and the standard deviation obtained on 2D grayscale images. These four features were fed into a DMNN for classification of 3D objects. For testing, COIL-20 image database and a generated dataset were used. A comparative analysis of the proposed method with MLP and SVM is presented and the results reveal the advantages of the modified DMNN. An important characteristic of the proposed recognition method is that because of the simplicity of calculation of the extracted features and the DMNN, this method can be used in real applications.

KW - 3D object recognition

KW - Dendrite morphological neural network

KW - classification

KW - efficient training

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BT - Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings

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Modified dendrite morphological neural network applied to 3D object recognition

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